A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria

First light demonstration of the integrated superconducting spectrometer

Ultra-wideband 3D imaging spectrometry in the millimeter-submillimeter (mm-submm) band is an essential tool for uncovering the dust-enshrouded portion of the cosmic history of star formation and galaxy evolution. However, it is challenging to scale up conventional coherent heterodyne receivers or free-space diffraction techniques to sufficient bandwidths ($\geq$1 octave) and numbers of spatial pixels (>$10^2$). Here we present the design and first astronomical spectra of an intrinsically scalable, integrated superconducting spectrometer, which covers 332-377 GHz with a spectral resolution of $F/\Delta F \sim 380$. It combines the multiplexing advantage of microwave kinetic inductance detectors (MKIDs) with planar superconducting filters for dispersing the signal in a single, small superconducting integrated circuit. We demonstrate the two key applications for an instrument of this type: as an efficient redshift machine, and as a fast multi-line spectral mapper of extended areas. The line detection sensitivity is in excellent agreement with the instrument design and laboratory performance, reaching the atmospheric foreground photon noise limit on sky. The design can be scaled to bandwidths in excess of an octave, spectral resolution up to a few thousand and frequencies up to $\sim$1.1 THz. The miniature chip footprint of a few $\mathrm{cm^2}$ allows for compact multi-pixel spectral imagers, which would enable spectroscopic direct imaging and large volume spectroscopic surveys that are several orders of magnitude faster than what is currently possible.Comment: Published in Nature Astronomy. SharedIt Link to the full published paper: https://rdcu.be/bM2F

Seismic Exploration Using Active Sources at Kuchierabujima Volcano, Southwest Japan

Seismic exploration using artificial sources was conducted at Kuchierabujima volcano, southwest Japan in November 2004 by 40 participants from 9 national universities andJapan Meteorological Agency to investigate the subsurface seismic structure. The exploration was the 11th joint experiment under the National Project for Prediction of Volcanic Eruptions. A total of 183 temporal stations equippedwith a 2 Hz vertical component seismometer (including 75 3component seismometers) and a portable data logger were deployed on Kuchierabu Island. Dynamite shots with charges of 10-115 kg were detonated at 19 locations, and seismic signals were successfully recorded. To reveal the P-wave velocity structure, 2955 arrival times of the first motion were picked from the seismograms, and 2187 were classified into ranks A and B. From the record sections and the arrival time data, characteristics reflecting the geological structure were identified. Refracted waves of 5 km/s were observed at stations＞5km from the shot points. Apparent velocities near the shot points depend on the surface geology around the shots. P-wave arrived earlier at stations near the summits. Strongly scattered waves were observed similarly near the summits

Seismic exploration at Fuji volcano with active sources : The outline of the experiment and the arrival time data

Fuji volcano (altitude 3,776m) is the largest basaltic stratovolcano in Japan. In late August and early September 2003, seismic exploration was conducted around Fuji volcano by the detonation of 500 kg charges of dynamite to investigate the seismic structure of that area. Seismographs with an eigenfrequency of 2 Hz were used for observation, positioned along a WSW-ENE line passing through the summit of the mountain. A total of 469 seismic stations were installed at intervals of 250-500 m. The data were stored in memory on-site using data loggers. The sampling interval was 4 ms. Charges were detonated at 5 points, one at each end of the observation line and 3 along its length. The first arrival times and the later-phase arrival times at each station for each detonation were recorded as data. P-wave velocities in the surface layer were estimated from the travel time curves near the explosion points, with results of 2.5 km/s obtained for the vicinity of Fuji volcano and 4.0 km5/s elsewhere

Epigenetic basis of neuronal plasticity: Association with R/G-band boundaries on human chromosomes

Epigenetic mechanisms have been suggested to have roles in neuroplasticity, in particular with regard to learning and memory formation, and in a range of neural diseases. In addition to epigenetic marks, the human genome also contains large-scale compartmentalized structures that might also influence neuroplasticity and neural disease. These structures result from variations in the amounts of GC% and in the timing of DNA replication and give rise to longitudinal differentiation (light and dark bands) along chromosomes after the appropriate staining. Here we describe our current understanding of the biological importance of the boundaries between these light and dark bands (the so-called R/G boundaries). We propose that the R/G-band boundaries on human chromosomes can be altered by epigenetic mechanisms, and that these changes may affect neuroplasticity, which is important to memory and learning, and may also have a role in the development of neural diseases associated with genomic instability